With the abundance of multimedia data made available through various means in general and the Internet and world-wide web (WWW) in particular, there is also a need to provide effective ways of searching for such multimedia data. Searching for multimedia data in general and video data in particular may be challenging at best due to the huge amount of information that needs to be checked. Moreover, when it is necessary to find a specific content of video, the prior art cases revert to various metadata that describes the content of the multimedia data. However, such content may be complex by nature and not necessarily adequately documented as metadata.
The rapid increase in multimedia databases, accessible for example through the Internet, calls for the application of effective means for search-by-content. Searching for multimedia in general and for video data in particular is challenging due to the huge amount of information that has to be classified. Prior art techniques revert to model-based methods to define and/or describe multimedia data. However, by its very nature, the structure of such multimedia data may be too complex to be adequately represented by means of metadata. The difficulty arises in cases where the target sought for multimedia data is not adequately defined in words, or respective metadata of the multimedia data. For example, it may be desirable to locate a car of a particular model in a large database of video clips or segments. In some cases the model of the car would be part of the metadata but in many cases it would not. Moreover, the car may be at angles different from the angles of a specific photograph of the car that is available as a search item. Similarly, if a piece of music, as in a sequence of notes, is to be found, it is not necessarily the case that in all available content the notes are known in their metadata form, or for that matter, the search pattern may just be a brief audio clip.
A system implementing a computational architecture (hereinafter “The Architecture”) that is based on a PCT patent application number WO 2007/049282 and published on May 3, 2007, entitled “A Computing Device, a System and a Method for Parallel Processing of Data Streams”, assigned to common assignee, and is hereby incorporated by reference for all the useful information it contains. The Architecture consists of a large ensemble of randomly, independently, generated, heterogeneous processing cores, mapping in parallel data-segments onto a high-dimensional space and generating compact signatures for classes of interest.
A vast amount of multimedia content exists today, whether available on the web or on private networks. Efficiently grouping such multimedia content into groups, or clusters, is a daunting assignment that requires either appropriate metadata for clustering purposes, or manual completion by identifying commonalties for the clustering purposes. Difficulties arise when portions of multimedia content are not readily recognized for the purpose of clustering. For example, if a picture of the Lincoln Memorial in the sunset is not tagged as such, then only a manual search will enable a user to cluster this image with other pictures of sunsets.
Therefore, it would be advantageous to provide a solution for unsupervised clustering of multimedia content that would cure the deficiencies of prior art techniques.